System and method for segmenting crowded environments into individual objects

Abstract

A crowd segmentation system and method is described. The system includes a digital video capturing subsystem and a computing subsystem. The computing subsystem utilizes an emergent labeling technique to segment a crowd into individuals. The emergent labeling technique employs algorithms which can be used iteratively to place vertices associated with feature points in a captured digital video image into multiple cliques and, ultimately, in a single clique.

1 . A system for segmenting crowded environments into individual objects, comprising: 
 an image capturing subsystem; and    a computing subsystem, wherein said computing subsystem utilizes an emergent labeling technique to segment a crowded environment into individual objects.    
   
   
       2 . The system of  claim 1 , wherein said image capturing subsystem is configured to detect feature points of objects of interest.  
   
   
       3 . The system of  claim 2 , wherein said computing subsystem includes a computing component.  
   
   
       4 . The system of  claim 3 , wherein said computing component is configured to associate the feature points with vertices of a graph.  
   
   
       5 . The system of  claim 4 , wherein said computing component is configured to collect two or more of the vertices into one or more cliques.  
   
   
       6 . The system of  claim 5 , wherein said computing component is configured to assign each of the vertices to a single clique.  
   
   
       7 . The system of  claim 6 , wherein assignment of each of the vertices to a single clique is accomplished with a soft assign technique.  
   
   
       8 . The system of  claim 7 , wherein the computing component assigns the vertices to cliques through the use of both local context and a global score function.  
   
   
       9 . The system of  claim 7 , wherein the soft assign technique is utilized iteratively to accomplish assignment of each of the vertices in a single clique.  
   
   
       10 . The system of  claim 1 , wherein said image capturing subsystem comprises a digital camera.  
   
   
       11 . The system of  claim 1 , wherein said image capturing subsystem comprises an analog image capturing device and an analog to digital converter.  
   
   
       12 . The system of  claim 11 , wherein said analog image capturing device comprises a scanner.  
   
   
       13 . The system of  claim 1 , where said image capturing subsystem comprises a microscope.  
   
   
       14 . A system for segmenting crowded environments into individual objects, comprising: 
 a digital image capturing subsystem configured to detect feature points of objects of interest; and    a computing subsystem, wherein said computing subsystem utilizes an emergent labeling technique to segment a crowded environment into individual objects.    
   
   
       15 . The system of  claim 14 , wherein said computing subsystem includes a computing component.  
   
   
       16 . The system of  claim 15 , wherein said computing component is configured to associate the feature points with vertices of a graph.  
   
   
       17 . The system of  claim 16 , wherein said computing component is configured to collect two or more of the vertices into one or more cliques.  
   
   
       18 . The system of  claim 17 , wherein said computing component is configured to assign each of the vertices to a single clique.  
   
   
       19 . The system of  claim 18 , wherein assignment of each of the vertices to a single clique is accomplished with a soft assign technique.  
   
   
       20 . The system of  claim 19 , wherein the computing component assigns the vertices to cliques through the use of both local context and a global score function.  
   
   
       21 . The system of  claim 19 , wherein the soft assign technique is utilized iteratively to accomplish assignment of each of the vertices to a single clique.  
   
   
       22 . The system of  claim 14 , further comprising a microscope in communication with said digital image capturing subsystem.  
   
   
       23 . A method for segmenting a crowded environment into individual objects, comprising: 
 capturing an image of a crowded environment;    detecting feature points within the image of the crowded environment;    associating a vertex with each of the feature points; and    assigning each vertex to a single clique.    
   
   
       24 . The method of  claim 23 , wherein said capturing an image is accomplished with a digital image capturing device.  
   
   
       25 . The method of  claim 23 , wherein said capturing an image is accomplished with an analog image capturing device and an analog-to-digital converter.  
   
   
       26 . The method of  claim 25 , wherein said analog image capturing device comprises a scanner.  
   
   
       27 . The method of  claim 23 , wherein said capturing an image is accomplished with a microscope.  
   
   
       28 . The method of  claim 27 , wherein said capturing an image is further accomplished with an analog-to-digital converter.  
   
   
       29 . The method of  claim 23 , wherein said assigning each vertex comprises utilizing a soft assign technique.  
   
   
       30 . The method of  claim 29 , wherein the soft assign technique uses both a local context and a global score function.  
   
   
       31 . The method of  claim 30 , further comprising using an optimal labeling matrix to iteratively assign each vertex to a single clique.  
   
   
       32 . A method for segmenting an environment having multiple objects into individual objects, comprising: 
 digitally capturing an image of an environment having multiple objects;    detecting feature points within the image of the multiple objects;    associating a vertex with each of the feature points; and    assigning each vertex to a single clique and thereby segmenting individual objects from the multiple objects.    
   
   
       33 . The method of  claim 32 , wherein said digitally capturing an image is accomplished with a digital camera.  
   
   
       34 . The method of  claim 32 , wherein said digitally capturing an image is accomplished with an analog image capturing device and an analog to digital converter.  
   
   
       35 . The method of  claim 34 , wherein said analog image capturing device comprises a scanner.  
   
   
       36 . The method of  claim 32 , wherein said digitally capturing an image is accomplished with a microscope.  
   
   
       37 . The method of  claim 36 , wherein said digitally capturing an image is further accomplished with an analog to digital converter.  
   
   
       38 . The method of  claim 32 , wherein said assigning each vertex comprises utilizing a soft assign technique.  
   
   
       39 . The method of  claim 38 , wherein the soft assign technique uses both a local context and a global score function.  
   
   
       40 . The method of  claim 39 , further comprising using an optimal labeling matrix to iteratively assign each vertex to a single clique.  
   
   
       41 . The method of  claim 32 , wherein said detecting feature points comprises: 
 generating a probabilistic background model; and    selecting high temporal and/or high spatial discontinuity image locations as the feature points.    
   
   
       42 . The method of  claim 32 , wherein the number of multiple objects is unknown.